Socia Networks and Political Participation_ the Role of Social in(2)

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Southern Illinois University Carbondale

OpenSIUC

Publications Department of Political Science

1-1-2003

Socia Networks and Political Participation: TeRole of Social Interaction in Explaining Political

ParticipationSco D. McClurgSouthern Illinois University, [email protected]

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Recommended CitationMcClurg, Sco D., "Socia Networks and Political Participation: Te Role of Social Interaction in Explaining Political Participation"(2003).Publications. Paper 6.hp://opensiuc.lib.siu.edu/ps_pubs/6
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Social Networks and Political Participation:

The Role of Social Interaction in Explaining Political Participation

Scott D. McClurgAssistant Professor

Department of Political ScienceSouthern Illinois University3165 Faner HallMailcode 4501

Carbondale, IL 62901-4501

Published in

Political Research Quarterly, 2003, 56(4):448-65.

An earlier draft of this paper was presented at the Annual Meeting of the MidwestPolitical Science Association. Palmer House, Chicago, IL. April 15 18, 1999. I wouldlike to thank Brady Baybeck, Scott Comparato, J. Tobin Grant, Bob Huckfeldt, JanLeighley, John Sprague, and the anonymous reviewers for their helpful advice and gentlecriticisms. Ken Goldstein also provided insightful comments on a much earlier draft ofthis paper. Toby Bolsen was helpful in proofing the final manuscript. All remainingerrors are the sole responsibility of the author.

This is a pre-typeset version of a peer-reviewed paper published in Politics ResearchQuarterly developed for deposit on the SIUC institutional repository. All referencesshould refer to the published version, details given above.


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Social Networks and Political Participation: The Role of

Social Interaction in Explaining Political Participation

The argument advanced in this paper is that interaction in social networks has a strong,though often overlooked, influence on the individual propensity to participate in politics.

Specifically, I argue that social interaction creates opportunities for individuals to gatherinformation about politics that allow them to live beyond personal resource constraints,thereby supporting the political activity of many people. Using relational data from theSouth Bend election survey, this paper provides evidence that the effect of socialinteraction on participation is contingent on the amount of political discussion that occursin social networks. Additional analysis shows the substantive and theoretical importanceof such interaction by explaining how it is distinct from the effect of social groupmemberships and how it enhances the effect of individual education on the probability ofparticipation. This key contribution of this paper is to show that models of politicalparticipation that do not account for informal social interaction will be theoreticallyunderspecified. It also shows that such interactions play a crucial role in explicating the

role of other factorsthat predict participation, such as group membership and individualresources.


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INTRODUCTION

Given the central role that scholars and casual observers attribute to citizen

participation in American democracy, it is no surprise that a great deal of effort has been

spent examining the causes of such activity. But untangling the theoretical thicket

surrounding participation has proved to be a trying task, with recent reviews of the field

observing that we have much left to learn about the causes of political involvement

(Leighley 1995; Schlozman 2002; Rosenstone and Hansen 1993). In response to such

observations, the analytic focus of participation scholars has started to move beyond a

narrow concentration on the individual characteristics and resources associated with

participation, specifically by devoting greater attention to role the environmental

determinants of involvement. Despite this trend, one area that still receives little

attention is the influence of interaction in social networks on individual levels of

participation.

One reason for this inattention is that social interaction is seemingly ubiquitous

and may not provide much leverage in sorting participants out from non-participants.

Another reason is that existing scholarship highlights the importance of formal social

interaction, such as membership in voluntary groups, as a cause of involvement.

Consequently, there may be a tendency to assume that the social underpinnings of

participation are effectively controlled for once formal group memberships are

accounted for in empirical analyses.

This paper seeks to rectify this shortcoming by testing the implications of a social

network model of political involvement. Three questions are addressed. First, when and

how do social networks make people politically active? Second, is the impact of informal


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interaction in those networks distinct fromthat of formal social organizations? Finally,

how much does a social network model of involvement add to our theoretical and

substantive understanding of how people become involved in politics?

To address these questions, I first outline a social network model of participation

that emphasizes the substance rather than theform of social interaction as the key to

unlocking social network influences on participation. This model is then used to outline

predictions about the circumstances under which informal interaction should influence

participation, thereby highlighting the usefulness of social interaction as a theoretical tool

for studying involvement. The model is also used to demonstrate that social interaction

has a value-added effect that helps us better understand when personal characteristics and

resources contribute to involvement. Using relational data from the South Bend election

survey, this paper provides evidence that social networks only influence participation

when they carry political substance, that this effect exists even when controlling for

membership in formal social institutions, and that even the effect of individual resources

cannot be fully understood without accounting for this process.

SOCIAL INTERACTION AND POLITICAL PARTICIPATION

Previous research

Traditional explanations of political participation focus attention on the individual

characteristics that distinguish participants from non-participants, such as levels of

education and income. But the empirical limits of those explanations have led to

renewed interest in the environmental foundations of political involvement (Leighley

1995; Rosenstone and Hansen 1993). In terms of sociological causes of action, this has

led to a considerable body of research investigating forms of formal social engagement,


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such as membership in civic groups, churches, and the workplace (Verba et al. 1995;

Harris 1994; Radcliff and Davis 2000; Calhoun-Brown 1996; Putnam 2000; Leighley

1996; Olsen 1972; Pollock 1982; Sallach et al. 1972; Ayala 2000). Explanations for the

relationship between membership in social organizations and political involvement

includes arguments that the membership stimulates a collective interest in politics (e.g.,

Putnam 2000), makes people available to elites for mobilization (e.g., Leighley 1996),

and helps people learn skills that make participation easier (e.g., Verba et al. 1995).

In contrast, relatively little research investigates the importance of social

interaction that occurs in interpersonal networks. Huckfeldt (1979) and Giles and

Dantico (1982) show that individual participation in politics varies as a function of

neighborhood education, an effect attributed to social interaction in interpersonal

networks. Kenny (1992) illustrates that having friends who participate makes people

more likely to participate themselves, while other research demonstrates that the size and

political orientation of networks predicts electoral participation (Leighley 1990; Knoke

1990a, 1990b; Lake and Huckfeldt 1998). Other work indirectly implies that even basic

forms of interaction such as playing cards, attending dinner parties, or being married may

make people more likely to participate by increasing interpersonal trust and adherence to

social norms (Timpone 1998; Putnam 2000, Chapter 1). Similarly, there is evidence that

patterns of family interaction can help explain patterns of participation (Burns et al.

2001).

The failure to adequately investigate social network effects on involvement may

be attributed to two trends, both of which are rooted in the absence of a clear theoretical

link between social networks and involvement. The first is an implicit belief that formal


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and informal social interaction can be lumped together under the rubric of social capital.

Yet scholarly explanations of group effects and social network effects imply that they

should influence behavior in distinct ways. Social network theorists see informal

interaction as being important because it exposes people to stimuli that are social in

origin and distinct from individual development. By contrast, explanations for

organizational effects focus on the development of civic skills (Verba et al. 1995; Ayala

2000) and availability for mobilization (Leighley 1996). Neither of these explanations

for formal membership effects emphasizes the same factors as the social network

argument. And even if formal organizations expose people to the same social stimuli that

interest network theorists, this has not been the focus in the literature on participation and

we likely underestimate the importance of such factors.

It is also possible that informal social interaction is seen as a weak theoretical tool

for explaining participation. Even in light of the apparent decline in social involvement,

informal social interaction remains ubiquitous. The implication is that, if everyone

engages in social interaction, it cannot be used to sort participants from non-participants.

Even though the aforementioned research by Huckfeldt, Knoke, Kenny, and Leighley

belies such a conclusion, it is clear that we need a better understanding of how informal

interaction influences electoral participation. Simply stated, we need an empirically-

validated model that identifies when informal social interaction supports involvement and

when it does not.

A Social Network Model of Participation

The two shortcomings mentioned here both stem from the absence of an

empirically-tested, micro-sociological model of participation. Here I draw on an


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approach which postulates that social exchange variably exposes people to a social

supply of information that broadens their exposure to and understanding of politics

(Huckfeldt and Sprague 1995; McPhee 1963; Huckfeldt 2001, 1984, 1983). Based in the

tradition of contextual research, this approach has been used to extensively and

effectively study many political behaviors other than political participation, such as vote

choice (Huckfeldt and Sprague 1995, 1988; Beck et al. 2002) and public opinion (Kenny

1994; MacKuen and Brown 1987; Huckfeldt et al. 1995, 1998).

The main tenet of this approach is that informal conversations between network

partners expose people to political information from the surrounding social environment.

Extrapolating to participation, the implication is that social interaction can make people

more active in politics when it exposes them to politically-relevant information.

Conceptually, social discourse exposes people to a wide range of information that may

influence participatory decisions, such as information about the desirability of

participation. Discussions with friends who are interested or active in politics can help

people learn about the reasons for participating while reinforcing the idea that such

behavior is desirable among ones peers. People also may be exposed to information

about the mechanics of electoral politics and involvement. Information about which

candidate to support, why to support that candidate, when the candidate is holding a rally,

or even how to just get involved are all types of information that can be effectively

exchanged by word-of-mouth.

Social interaction exposes people to a different set of politically-relevant

information and stimuli than they possess individually (Huckfeldt 2001; Mutz 2002a,

2002b). Since individual understanding, information, resources, and ability are


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inherently limited, this means that social interaction provides people with another

opportunity to accrue resources that lower the barriers to political participation.

Consequently, social resource supplement (rather than supplant) the person resources and

abilities that make participation likely.

By outlining a social mechanism by which social networks influence participation

conversations between people and defining when it should influence their behavior

when politically-relevant information is exchanged this model places a clear emphasis

on the substance,rather than theform, of social interaction. Such an approach has three

advantages for understanding participation. First, it is flexible enough to allow many

different socialformsto influence behavior ranging from marriage to friendship to

membership in formal organizations without losing its explanatory power. Second, it

does not directly contradict previous findings explaining the relationship between formal

social groups and participation. Together, these features imply that there is more than

one way that sociological factors can influence participation. Third, the model can be

exploited to develop meaningful hypotheses about the relationship between social

networks and involvement because not all social interactions will influence participation.1

The Value-Added Effect of Social Interaction

Although the preceding discussion implies that social resources function similarly

to individual resources, sociological theory also suggests that social interaction has a

second benefit it facilitates the application of individual resources to collective

behavior. A classic statement of this can be found in Colemans (1988, S109-S113)

discussion of how family life impacts a childs education where he argues that people

1This somewhat contrasts some work on social capital which suggests that all forms social interactionpromote a shared sense of community or interpersonal trust, both of which may support politicalinvolvement.


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with more access to social resources find it easier to apply their own personal resources

towards furthering their childs education.

The importance of this can be seen by contrasting a social network approach with

research on the individual characteristics that drive participation. The latter literature,

which has dominated research on participation for years, argues that individuals of higher

social status are more likely to participate than lower status people because they have

resources that make participation easier for them. The social network model makes a

similar argument in that low status people may still become politically active if they

accrue social resources. As such, social resources may close the participation gap that

exists between low and high status individuals. However, the network model also

insinuates that this gap may exist in part because social resources exacerbate the

differences because they facilitate the application of human capital toward political

activity.

This second possibility is important because it shifts our theoretical conception of

how resources influence activity. If social resources do not have the added-value effect,

the implication is that people must pass a resource threshold in order to participate once

individuals get enough resources, personal or social, they will participate. In this case,

social interaction would merely be another resource that makes participation more likely.

But if social interaction does have the added-value effect, then we should see a

curvilinear effect and the combination of high individual resources and high social

resources will widen this participation gap. This implies that we must not think of

resources simply in terms of how much but also in terms of what type.


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DATA AND MEASURES

To establish the theoretical and substantive importance of social networks in

explaining participation, I use survey data gathered in South Bend, Indiana during the

1984 presidential election (for details see Huckfeldt and Sprague 1985; 1995, Chapter 1).

The South Bend Study is appropriate because it was designed to measure social

influence. This is accomplished by gathering the names of people with whom

respondents discussed political matters, therefore yielding information on theirpolitical

networks. I focus the analysis on the impact of social interaction by using a subset of the

South Bend respondents for which interviews of the discussants were also completed.

Following other work in this area the unit of analysis is respondent-discussant pair, or

discussion dyad. These data help isolate the social process being examined though they

have limited external validity and future work should examine these questions in a

broader context.

Dependent Variable. Each main respondent was asked whether he or she had

worked for a candidate in the election, attended a meeting or rally, put up a political sign

or bumper sticker, or donated money (see Appendix A for variable descriptions).2 An

index of electoral involvement was created by adding together each of these dichotomous

variables, where a 1 signified participation and a 0 signified non-participation. As a

measure of participation in election campaigns, this index serves as the dependent

variable in the analyses below.

2Over eighty-percent of the respondents reported voting, a highly suspicious number given aggregateturnout in American elections. However, it is not surprising since research demonstrates the socialdesirability issues lead people to over-report voting (Clausen 1968; Silver et al. 1986). As a result, thedichotomous variable measuring whether a respondent voted was not used because it is unreliable. Nocomparable evidence exists to suggest that the other measures are susceptible to the same bias andoverreport problems, so I use them in measuring participation.


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Figure 1 shows that a majority of the respondents do not participate in even one

political act beyond voting. Among those who are involved, most people only participate

in one of the four activities. The graph also shows that the dependent variable does not

have a standard normal distribution. The typical response to a dependent variable of this

type is to use a model for count data, the most common of which is the Poisson

regression model. Yet this model assumes that people with the same independent

variables are expected to exhibit the same number of activities (Long 1997, p. 221-3), an

assumption not supported by a hypothesis test of for overdispersion (probably reflecting

unobserved heterogeneity). As a result, statistical estimates are obtained with the

negative binomial regression model (Long 1997, p. 235-7; King 1988, 1989).3

[Insert Figure 1 about here]

Measures of Social Interaction. Each survey respondent was asked to name up to

three people with whom he or she discussed politics.4 Respondents were then queried

about the nature of their discussion with these people. One question asked the respondent

to report how often he or she spoke with each discussant, a measure of generic social

interaction. The second question asked how frequently the respondent and the discussant

discuss politics, a measure of political interaction. For both variables, respondents gave

one of four answers never, once in a while, sometimes, or fairly often.5 These

questions provide measures of social interaction in the discussion dyads.

3Overdispersion can be the product of either unobserved heterogeneity or contagion, where a singleactivity makes another more likely (Long 1997). A clear treatment of the key assumptions underlying thenegative binomial model can be found in Long (1997), Greene (1997), and Cameron and Trivedi (1986, pp.33-34).4For purpose of clarification, it is important to point out that every discussant is technically a politicaldiscussant because of how they names were collected. But Figure 2 shows that being identified as apolitical discussant does not mean that political conversation occurs between the respondent and discussant.5These responses are numerically coded to range from 0 (never) to 3 (fairly often).


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Figure 2 displays the histogram for both discussion measures. Not surprisingly,

people have frequent social conversations with the people in their networks with 90-

percent of all respondents talking to their discussant sometimes or fairly often. In

contrast, explicitly political interaction is relatively low with a majority of individuals

only talking politics once in a while. A chi-square test shows that political interaction

and social interaction are not independent of one another (2=31.1588, p


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The Substance of Social Interaction as an Influence on Political Involvement

I begin by testing the hypothesis that social interaction only affects participation

when there is an exchange of political information. To examine this hypothesis I

estimate two separate statistical models of electoral involvement. Both models include

variables identified as important for understanding participation, including controls for

socioeconomic status, politically-relevant attitudes, generalized civic engagement, and

political mobilization.6 The difference between the two models lies with the measure of

social interaction. In the generic model, social interaction is measured as the frequency

with which the respondent reported talking with his or her named political discussant,

with no reference to political substance. In the political model, social interaction is

measured as the frequency of political discussion. If the hypothesis is correct, then social

interaction will only be statistically significant in the second model.

Although it may seem relatively clear that political conversation should predict

participation and generic social interaction should not, the demonstration here is

important for two reasons related to a sociological understanding of political

involvement. First, the terms social capital, civic engagement, and civil society are

often used to describe a wide variety of social phenomenon. Moreover, the typical claim

is that more social capital or greater civic engagement increases an individuals

likelihood of becoming politically-active. Yet the model outlined here suggests a more

narrow and useful specification. Additionally, both statistical models include factors that

are sometimes believed to capture social influences on participation, mainly marital status

and formal group membership. As such, the specification represents a relatively clear

6Family income has not been included in these models because it is strongly collinear with education andhas the effect of reducing the statistical precision of the coefficient associated with education. Includingincome in these models does not change any of the conclusions offered in this paper.


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test of the proposition that informal social interaction influences participation beyond the

effects of formal groups, but only when that interaction revolves around the exchange of

political information as reported through survey interviews.

Negative binomial parameter results for these two models are displayed in Table

1. The results are largely consistent with each other in that education, party mobilization,

and membership in an organized group are statistically significant predictors of campaign

participation. Most importantly, the results show that social interaction is only

statistically significant when it explicitly contains political substance, evenin the

presence of measures of group membership and activity. This highlights two things that

directly confront the reasons why informal social interaction does not receive as much

attention as other forms of social engagement. First, social exchange exerts a positive

and statistically precise effect on participation, but only when it is politically-relevant.

So even if informal interaction builds social capital with all of its potential benefits, that

social capital is only relevant to politics in particular circumstances (Lake and Huckfeldt

1998). Second, this effect exists even after controlling for membership in organized

groups, supporting the earlier argument that formal and informal social interaction have

theoretically distinct effects on involvement.

[Insert Table 1 about here]

One surprising result surfaces in Table 1. Interest in the campaign is a

statistically significant predictor of political participation in the social interaction model,

but not in the political interaction model. This is likely due to the fact that political

discussion is related to a respondents interest in political topics. Yet previous research

shows that political discussion is not purely driven by political interest, but also by the


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motivations of the other conversant, perceived levels of political knowledge, and shared

political viewpoints (Huckfeldt 2001). Moreover the relationship between political

conversation and campaign interest is likely reciprocal. People who are interested in

politics undoubtedly bring it up more in their social conversations. Likewise, people who

are exposed to political conversations may themselves become more interested in the

subject matter. In short, these factors are clearly related to, but not synonymous with,

each other. Excluding either from a model of participation runs the risk of omitted

variable bias in the others coefficient, while including both increases the risk of

colinearity and hence statistically insignificant results.

Substance versus Form

Even though Table 1 shows that the substanceof social interaction influences

electoral involvement, it does not show that theformof social interaction is irrelevant.

Although they show that group membership effects are distinct from network effects,

they say nothing of how network formmight influence participation. As a further test of

the models argument that substanceandpolitical relevanceof social interaction drives

the effect, it is important to show that the results are not solely driven by interaction in

particular types of networks, especially those based on intimate social relations.

Such an investigation has additional benefits. Prior research implicitly

emphasizes network form, such as Timpones (1998) excellent analysis of the importance

of marital status as a predictor of voter turnout or Putnams (2000) work on social capital,

without investigating the exchanges taking place in networks. Examining the earlier

results for different types of dyads is an initial step in expanding the discussion of this

issue. This highlights the potential benefits of exploring the social network model in


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more detail. Questions about family networks (strong ties) versus less intimate networks

(weak ties) are typical in the social network literature, but are not part and parcel of the

participation literature even though evidence has shown the importance of marital status

(Grannovetter 1973; Burt 1987).7 Consequently, examining network form illustrates how

a social network model can stimulate more empirical investigation into the causes of

activity.

Following in the tradition of Huckfeldt and Sprague (1995; see also Burt 1987),

the model outlined here predicts that people who are exposed to similar information via

social interaction will exhibit similar behaviors. Thus the model predicts that political

conversations should buttress political participation regardless of the relationship

between the individual and her discussant. At the same time, this model does not

necessarily imply that form is entirely irrelevant. Indeed, it may be that the strength of a

substance effect may vary in different types of relationships because information coming

from more intimate associates is weighed more heavily than that provided by

conversations with friends.

I begin by examining the frequency of political discussion in cohesive dyads

interactions between spouses and family and non-cohesive dyads interactions with

casual acquaintances. One way that form may trump substance and undercut the model

used here is if political conversation only occurs in cohesive (i.e., family) rather than

non-cohesive (i.e., friends) discussion dyads. Table 2 provides some evidence that

political discussion is somewhat more frequent among spouses than it is among family

and friends, with 31-percent of the marital dyads having political conversations fairly

7Knoke (1990b) is an excellent example of a study that belies this conclusion. He suggests that networkform is largely irrelevant when compared to the politics of the network.


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often. Only 14-percent of family dyads and 22-percent of friendship talk politics with

such frequency. Nevertheless, political discussion is not restricted to cohesive dyads.

The modal category for all three types of dyads spousal, familial, and friendship is to

discuss politics once in a while with the second highest category being fairly often.

Moreover, the amount of political conversation in friendship dyads is slightly higher than

it is in family dyads. In short, people are can exposed to political in all types of social

relationships.


Another way that form could matter over substance is if political discussion only

influences participation in specific types of dyads. Table 3 examines this possibility by

re-estimating the political interaction model for three types of discussion dyads

spouses, family members, or friends/acquaintances. The results provide evidence that

both the form and substance of conversation matter. Most importantly, in terms of the

model presented here is that political discussion is a statistically significant predictor of

involvement in all types of dyads, despite relatively low numbers of observations. These

results are consistent with Knokes (1990b) observation that the content of interaction is

the key to understanding network effects on participation. Consequently, the findings

demonstrate that political influence is not restricted solely to family contacts. If a person

grows up in an apolitical family, for instance, the potential for political mobilization still

exists. Since other evidence suggests that friendship ties expose people to more

heterogeneous political ideas and promote political tolerance (Huckfeldt 2001; Mutz

2002b; Grannovetter 1973), it is encouraging to see that those conversations also make

participation more likely.


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This conclusion does not mean that the effect of content is unrelated to network

cohesion. The coefficient associated with political interaction is about 50-percent larger

for spouses and family members than it is for friends and acquaintances. Although we

cannot make too much of this difference without more extensive analysis, it implies that

the relationship between network cohesiveness and involvement may be worth further

investigation. This conclusion is different from that offered by Knoke (1990b) in his

analysis of General Social Survey data, though his perceptual measure of a discussion

partners closeness may account for the difference.


THE SUBSTANTIVE IMPORTANCE OF SOCIAL INTERACTION

So far the analysis implies that models which do not account for informal social

interactions are underspecified in a theoretical sense. But illustrating theoretical

relevance of a concept is not the same as demonstrating that it is substantively important.

In this section, the goal is to illustrate that social networks play a substantively crucial

role in the process which produces electoral involvement.

The primary challenge here revolves around choosing a standard for judging

substantive importance. I take the approach of comparing network effects to the

substantive effect that individual resources, measured as years of education, have on

individual participation. As noted earlier, socioeconomic status is an important predictor

of political involvement, largely because it measures individual resource constraints

(Verba et al. 1995; Nie et al. 1996). It is also, however, an imperfect predictor, with

significant proportions of low status people participating and high status people staying

out of politics. By showing how informal social interaction help explain the behavior of


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these anomalous groups in a way that is substantively meaningful, I can illustrate the

importance of this concept for our understanding of participation.

Social Interaction, Low-Status Individuals, and Political Activity. The model

offered in this paper posits that social interaction should be substantively important for

people with and without personal resources. When considering the behavior of low status

individuals this means that social interaction should make up the absence of personal

resources and we should see a meaningful increase in the propensity to participate among

low status individuals who discuss politics. To examine the effect of political discussion

for this group, I calculate number of political activities a person with a high school

diploma is expected to engage in while varying the level of political conversation. More

specifically I produce a distribution of expected values using a method suggested by King

et al. (2000) for interpreting statistical results (more details are provided in Appendix B).8

In producing these graphs, all of the other independent variables are set equal to their

expected values for an individual with twelve years of education (see Table B in the

Appendix B).

These distributions are displayed in the panels of Figure 3. In each panel the solid

line shows the expected value distribution for individuals who do not talk politics with

their discussion partner. The dashed line in each panel is the expected value distribution

of participation where level of discussion increases from fairly often (Panel A), to once in

a while (Panel B), and then to most times (Panel C). The important message in this

figure is that political discussion has a substantively strong influence on the participatory

behavior of low status individuals. A low status person who never talks politics with his

8This method uses statistical simulation to produce a distribution of expected value for the dependentvariable. By changing values of the independent variables to produce these expected value distributions,the substantive effects can be effectively discerned through graphical analysis (Cleveland 1993).


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or her discussant is expected to engage in approximately .16 political activities.

Conversely, the average expected value of participation increases to .68 for a low status

person who talks politics most times with his or her discussant, almost a four-fold

increase. Similarly, increasing the level of political discussion by one category produces

a one-and-a-half factor increase in the level of participation, the same effect as increasing

education from a high school diploma to a college degree.

Interestingly, the uncertainty associated with this prediction visually depicted by

the spread of the curve increases with levels of political discussion, meaning that low

status person still may not become involved even when they talk politics with

considerable frequency. Nevertheless, the results indicate that low status individuals who

gain political information from their social network have a substantially higher chance of

becoming engaged in electoral politics than their counterparts who do not talk politics.


Figure 3 also shows that the marginal effect of discussion on participation

increases with levels of political discussion. Moving from the never talks politics

category to the talks politics once in a while category increases the average expected

level of political activity for low status people by .10 units, while moving from the once

in a while category to the fairly often category increases the average expected value by

.16 units. Finally, moving from the fairly often to the most times categories increases

expected political activity by .26 units.

Social Interaction, High Status Individuals, and Political Activity. Figure 4

depicts the effect of political discussion on the activity of relatively high status

individuals by examining the effect of political discussion on the expected value of


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participation for individuals with sixteen years of education (approximately a college

degree). The other independent variables are set equal to their expected value for

individuals with sixteen years of education.

The general pattern between the panels of Figure 4 resembles that in Figure 3

the difference in the expected value of political activity noticeably increases with the

level of political discussion. As before, going from the lowest level of political

discussion to the highest increases the expected level of participation by a factor of four.

And the marginal effect of discussion again gets stronger with the level of political talk.


This figure provides one additional insight about the substantive impact of social

networks on participation. In examining Panel A, for instance, we can see that the

predicted level of activity for these high status individuals is still relatively low (below

.5). In fact, the mean of the two expected value distributions in Panel A (.27 and .41,

respectively) are both below the sample average! In other words, people who are

relatively better off in terms of individual civic resources (measured here with education)

are still not very likely to participate unless they engage in politically-relevant exchanges

with other people. The benefits of a high social status are by themselves insufficient for

producing high levels of political activity.

Social and Personal Foundations of Political Participation. Although the results

show that informal social interaction has a strong substantive impact on participation, the

model posited above implies that high status individuals are more likely to benefit from

politically-oriented social interaction than low status individuals. In other words, social

resources are posited to have a value-added effect on participation in that they make


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people more likely to employ their personal resources toward political participation. This

suggests that there is a curvilinear dependency between individual and social resources in

explaining participation.

The expected level of involvement is always greater for high status people than

for low status people, undoubtedly reflecting the effect of higher status and its concurrent

individual resources. But does political discussion contribute to the participation gap

between low and high status people? Or, does political discussion help place them on

more equal ground? If the first scenario applies, it would be consistent with the value-

added hypothesis that social resources make individual resources more substantively

valuable. If the second scenario applies, then it would imply that social and individual

resources function in exactly the same manner, with a certain level of resources of

whatever type are necessary to encourage participation and once an individual has

them, she will likely become engaged (again, a threshold effect).

Table 4 reports the means for all of the simulated expected value curves displayed

in Figure 3 and Figure 4. Going down the first two columns, we see that the factor

change of increasing conversation is relatively constant the pure effect of discussion on

participation. But examining the third column, we see that the difference between means

of the expected value distribution increases with the level of the discussion variable. This

demonstrates that integration into the social structure serves to exacerbate the effects of

status. Among people who never talk politics in their dyads, high status people are

expected to engage in roughly .10 more activities than low status people. This gap

increases to .36 more activities becauseof the impact of social discussion.


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This result suggests that participation is not simply a matter of endowing people

with resources. Personal resources must be combined with social resources in a way that

encourages political participation for people to become active in politics. This is

evidence that social factors are especially important for people who possess human

capital. Although people who have little personal resources benefit from social

interaction, those substantive benefits pale in comparison to those experienced by high

status individuals. So while a social network model helps explain the behavior of two

anomalous groups (low status participants and high status non-participants), this

demonstration also shows that we cannot fully understand the importance of even

individual characteristics without accounting for the micro-sociological environment

surrounding individuals. As such, it implies that the social dimensions of participation

are crucially important also for understanding the impact of individual resources.

DISCUSSION

Experience shows that attention to the importance of social networks for

explaining participation does not always meet their ascribed importance. For example, a

substantial body of work focuses on explanatory factors that are best understood as

individual characteristics, including early research on socioeconomic status and later

work investigating civic resources and the psychological underpinnings of involvement.

Among the body of work that does examine environmental factors, there is a

preoccupation with features of the political context and formal group occupation. Both

sets of literature tend to de-emphasize or, at least, do not prioritize the importance of

social networks in understanding involvement. Just as these scholarly literatures provide

substantial insight, they also direct our attention away from another factor that is also


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crucially important the social underpinnings of political action. This paper illustrates

that a failure to incorporate social network factors in to our models of participation has

led to a misunderstanding of how group memberships, network intimacy, and individual

resources contribute to involvement. Most importantly, it highlights the fact that social

influences on participation are worthy of detailed and extensive inquiry as well.

Along these lines, this paper builds on previous research by providing a more

solid conceptual foundation for this kind of work. Specifically, the results presented here

have important implications for the manner in which empirical scholars treat social

effects in models of participation. For example, one common approach to controlling

for social effects is to include broad measures of social connectivity, such as marital

status (Timpone 1998), or measures of civic engagement, such as church attendance and

group membership (Olsen 1972; Pollock 1982; Sallach et al. 1972). Not only do the

results demonstrate that the first measure only roughly controls for the social process

underlying participation, but it illustrates that social interaction effects are not

synonymous with group membership effects. Overemphasizing the importance of such

group memberships without acknowledging more informal social processes may

undervalue the impact of social forces on participation.

One reason for this is that membership in formal social organizations has been

declining for five decades (Putnam 2000). If these membership effects were equivalent

to all social effects on participation, this would imply that the importance of the social

environment was in decline. Another reason is that a common explanation of group

membership effects is that they provide individuals with opportunities to develop

individual civic skills (Verba et al. 1995). A primary message of this paper is that we


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should look beyond such resources if we are to improve our understanding of how people

become politically active. If the results hold more generally, they imply that a full

accounting of process producing participation must examine the substance of social

interaction more closely in addition tomembership in civic institutions.

Additionally, there is evidence that not all forms of social interaction are

important. One claim made by proponents of social capital is that social involvement

exposes people to community norms and promotes interpersonal trust, factors which in

turn make political involvement more likely. Although the model and results outlined

here do not contradict those claims, it does provide a mechanism deriving hypotheses

about when social networks should support political action. It also helps promote a more

detailed understanding of the social foundations of participation, one that moves beyond

using rough measures of social interaction such as marriage.

More generally, the results highlight the potential pitfalls of over-individualized

models of political participation. Specifically they imply that any model that does not

account for the impact of politically-relevant social interaction will be underspecified.

Although there are some clear limits on the data used to examine these findings, they

illustrate that we may overestimate the importance of personal resources because their

application may rely on the types of social interaction experienced by the individual.

As a discipline, more attention should be devoted to unraveling the underlying

social dynamics that spur movement off of the sidelines and onto the field in electoral

politics. The model supported by the evidence here implies that one fruitful line of work

will examine implications stemming from the main assumption of the social network

model employed above that social interaction is important when it helps increase


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individual levels of political information. This assumption provides the foundation for a

potentially rich investigation of the social foundations of involvement. A second line of

inquiry is to explore the link between different types of networks, the substance of

discussion and involvement. The fact that political conversations are more influential

when carried on between spouses opens a number of questions about the relationship

between source-effectsand substance-effectsin promoting participation. Finally, this

paper suggests that we must think seriously about the factors that drive political

interaction in social networks.


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Figure 1. Histogram of the Number of Electoral Activities Respondents

Participated in During the 1984 Election. This figure displays the number of electoralactivities South Bend respondents participated in during the 1984 presidential election.This graph shows that the distribution of the dependent variable is non-normal. It alsoillustrates the relatively low level of electoral activity in the sample.


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Figure 2. Histogram of Two Different Types of Discussion. While people reportfrequent discussions of all types with their discussants, this figure shows that most surveyrespondents only report discussing politics fairly often or once in a while with theirdiscussants.


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Table 1. Parameter Estimates for a Two Negative Binomial Regression Model PredictingNumber of Electoral Activities. This table presents the parameter estimates of the relationshipbetween different types of social interaction that based on politics and that which is not andparticipation in electoral activities.

Independent Variable Generic Political

Interactiona

InteractionModel Model

Social Discussion 0.06

(0.54)

Political Discussion 0.46**

(3.96)

Years of Education 0.08* 0.09**

(2.45) (2.75)

Campaign Interest 0.31** 0.18

(2.57) (1.43)

Age 0.00 0.00

(0.09) (0.37)

Party contact 0.80** 0.85**

(5.14) 5.58

Church Attendance -0.06 -0.06

(-1.17) (-1.23)

Member of Organized Group 1.24** 1.24**

(3.36) (3.34)

Married -0.11 -0.17(-0.60) (-0.97)

Partisan Extremity 0.26** 0.28**

(3.15) (2.93)

Constant -4.21** -4.60*

(-5.38) (-6.51)

* 1.03** 01.03**

Likelihood Ratio 2 74.89** 74.89**

Number of Observations 537 537

Source: 1984 South Bend Election Study.

aThe dependent variable in this model is political activity, not

includinga measure of whether or not the respondent went to the polls,

during the 1984 campaign period.

*p


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Figure 3. Density Estimates of the Simulated Expected Value Distribution for

Respondents with Twelve Years of Education. These graphs illustrate the substantiveimportance of political discussion among high school educated individuals.

Notes: Simulations are based on the model reported in Table 1 using the measurementvalues given in the text. Simulations produced using CLARIFY (King et al. 2000; Tomzet al. 1998).


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Figure 4. Density Estimates of the Simulated Expected Value Distribution for

Respondents with Sixteen Years of Education. These graphs illustrate the substantiveimportance of political discussion among college educated individuals.

Notes: Simulations are based on the model reported in Table 2.1 using the measurementvalues given in the text. Simulations produced using CLARIFY (King et al. 2000; Tomzet al. 1998).


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Table 2. Level of Political Interaction within Different Types of Discussion Dyads.

This table shows the level of political discussion that takes place in different types ofdyads. Each cell shows the number of dyads within each column that exhibited aparticular level of discussion. Percentages are for column totals. This table shows thatthe level of political discussion is similar across types of relationships, except for the

lower level with family members.

Level of Type of Dyad

Political

Discussion Spouse Family Friend All

Never 3 7 26 36

(1%) (5%) (5%) (4%)

Once in 167 97 342 606

a While (61%) (75%) (66%) (66%)

Fairly Often 86 18 116 220

(31%) (14%) (22%) (24%)

Most Times 19 8 34 61

(6%) (6%) (7%) (6%)

Total N 275 130 518 923

% (101%)1 (100%) (100%) (101%)

Source: South Bend Data.

1Column percentages may not add up to 100% because of rounding error.


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Table 3. Parameter Estimates for a Three Negative Binomial Regression ModelsPredicting Number of Electoral Activities. This table presents the parameter estimates of therelationship between political interaction and participation in electoral activities, dependent on thetype of dyad relationship.

Independent Variable Spouses Family FriendsMembers

Political Discussion 0.58* 0.65* 0.39**

(2.25) (2.10) (2.98)

Years of Education 0.16* 0.17 0.05

(2.04) (1.88) (1.24)

Interest in Politics 0.12 0.04 0.17

(0.43) (0.14) (1.08)

Age 0.01 0.00 -0.00

(1.20) (0.31) (-0.70)

Party contact 0.12 1.05* 1.12**

(0.40) (2.42) (6.07)

Church Attendance -0.15 -0.29* 0.13

(-1.39) (-1.99) (0.21)

Member of Organized Group 1.79* 1.04 1.57*

(2.57) (1.31) (2.54)

Married ------ 1.42* -0.15

(2.42) (-0.76)

Partisan Extremity 0.21 0.12 0.32**

(1.19) (0.23) (3.29)

Constant -5.25** -6.30* -4.37**

(-3.40) (-3.23) (-4.80)

* 1.05** 0.21 0.52

Likelihood Ratio 2 21.98** 22.13** 81.13**

Number of Observations 153 81 304

Source: 1984 South Bend Election Study.

aThe dependent variable in this model is political activity, not

includinga measure of whether or not the respondent went to the polls,

during the 1984 campaign period.

*p.05 **p.01


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Table 4. Mean of the Simulated Expected Value Distributions.This tabledemonstrates that educational attainment is not the only reason that the expected value ofthe participation variable increases between Figure 3 and Figure 4.

Political Mean of the Simulated

Discussion Expected Value Distribution

High College Diff.a

School

Never 0.17 0.27 0.10

Once in 0.27 0.41 0.14

a While

Fairly Often 0.43 0.65 0.22

Most Times 0.68 1.04 0.36

Differenceb 0.51 0.77 0.26

Source: These are the mean values for these expected value distributions displayed inFigure 3 and Figure 4. Simulations are based on the political interaction model reportedin Table 1 using the measurement values given in the text. Simulations produced usingCLARIFY (King et al. 2000; Tomz et al. 1998).

aThis is the column difference for each row.

bThis is the row difference for each column.


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Appendix A Variable Descriptions and Coding

South Bend Data

These data were collected by Robert Huckfeldt and John Sprague. The survey was designed as a panel

study with a snowball component. There were three waves in which data was gathered on the mainrespondents, who are analyzed in this paper. Two of the survey waves took place prior to the 1984election. A third wave was administered soon after the election. There was replacement for observationsthat dropped out of the survey at each wave. The variables measuring income, education, and age wereadministered to main respondents during the survey wave in which they entered. The measures of politicalactivity, number of discussants, and party mobilization were all administered in the post-election wave.The remainder of this appendix describes each of these variables and reproduces the originalquestion usedto gather the data.

Political Activity

This measures how many of the following activities that a respondent engaged in during the 1984 electionseason: working on a campaign, attending a meeting or rally, putting up a political sign or bumper sticker,

or donating money to a party or candidate. The questions used to gather the information are listed below.Descriptive statistics are provided in Table A.

Did you work for any candidate in this election?(1) Yes (0) No

Did you go to any political meetings, rallies, dinners, or things like that?(1) Yes (0) No

Did you put up a political yard sign or bumper sticker during the campaign?(1) Yes (0) No

Did you give any money to a political party or candidate?

(1) Yes (0) No

Number of Discussants

This variable measures how many people the respondent reported discussing politics with. As noted in thetext, this variable was coded using a set of questions about how the respondent knew each discussant. Thequestion used to gather this information is listed below. Descriptive statistics are provided in Table A.

Is a member of your family? (I mean, is related toyou in any way by marriage or blood?)(1) Not related (2) Spouse (3) Mother or Father(4) Brother or sister (5) In-laws (6) Son or Daughter

(7) Other blood relative

(If not related:) How did you get to know ?(1) Work (2) Church (3) Neighborhood(4) Family (5) Republican Party (6) Democratic Party(7) Other organization (10) Politics (11) School(12) Children in school together (13) Friend of family (14) Casual social sit.


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Years of Education

This is a straightforward question about how many years the main respondent had been educated. Thesurvey question is provided below. Descriptive statistics are provided in Table A.

What is the highest grade of school or year of college you have completed?

Interest in Politics

This is a measure of each respondents level of interest in politics. The survey question is listed below.Descriptive statistics are provided in Table A.

And how much interest did you have in this years election?

(0) None at all (1) Only a little (2) Some

(3) A great deal

Income

This variable measures each respondents income level by categories. The survey question is listed below.Descriptive statistics are provided in Table A.

Last year, before taxes, was your total family income (response categories read):

(1) Under $5000 (2) $5000 - $10,000 (3) $10,000 - $15,000

(4) $15,000 - $20,000 (5) $20,000 - $30,000 (6) $30,000 - $40,000(7) $40,000 - $50,000 (8) $50,000 and over.

Age

Each respondent was asked what year he or she was born in. The age variable was coded by subtractingthat number from 1984. The survey question is listed below. Descriptive statistics are provided in TableA.

In what year were you born?

Party Mobilization

This is a post-election measure that asks each respondent whether he or she was contacted by a politicalparty during the election. The survey question below was re-coded as a three point measure to reflectwhether a respondent was contacted by both major parties, one major party, or neither major party.Descriptive statistics are provided in Table A.


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As you know, the political parties try to talk to as many people as they can to them to vote for theircandidate. Did anyone from one of the political parties call you up, or come around and talk to you aboutthe campaign this year?

(1) Yes (2) No

(If yes:) Which party was that?

(1) Republican (2) Democrat (3) Both

(4) Other


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Table A. Descriptive Statistics. This table presents the descriptive statistics for each ofthe variables used to produce the statistical estimates in Table 2 of the paper.

Variable Mean Stand. Min Max NDeviation

Political 0.45 0.87 0 4 1502

Activity

Number of 1.04 1.11 0 3 2158

Discussants

Years of 12.99 2.52 2 17 2150

Education

Interest in 2.45 0.76 0 3 1507

Politics

Income 4.54 1.95 1 8 1931

Age 50.30 15.66 18 98 2129

Party contact 0.31 0.60 0 2 2158

Source: 1984 South Bend Election Study (Huckfeldt and Sprague 1985).


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APPENDIX B: INTERPRETING STATISTICAL RESULTS WITH STATISTICAL SIMULATION

DescriptionThis appendix describes a method for interpreting statistical results developed by Gary King, MichaelTomz, and Jason Wittenberg (1998). In addition to developing this method, the authors have providedsoftware for implementing it in STATA (Tomz et al. 1998).

MethodKing et al. (1998) argue that there are two types of uncertainty in statistical results. One of these types isfundamental uncertainty. This form of uncertainty is accounted for in statistical results with the stochasticcomponents of models. A second form, labeled estimation uncertainty, refers to the fact that we haveimperfect knowledge about population parameters. In other words, the point estimates that come fromstatistical procedures are draws from a distribution around the true population parameter (see equation [1]).The problem, according to King and his co-authors, is that interpretation rarely accounts for this latter formof uncertainty.

[1] *))(**,(~ VN

In order to rectify this problem, King et al. (1998) suggest using a simulation method to incorporate

estimation uncertainty into substantive interpretation. This method assumes that the vector of parameterestimates in a statistical model, *, are a draw from a normal distribution around the true populationparameter,. The algorithm proceeds as follows:

1. Record parameter estimates from a statistical model;2. To incorporate estimation uncertainty, draw a value from the distribution of to

represent a parameter estimate;

3. Choose values for the independent variables at which you will compute anexpected value of the dependent variable;

4. Using the simulated coefficients from step 2 and using a draw from the modelsstochastic distribution, simulate an expected value of the dependent value for the setlevels of the independent variables.

By repeating each of these steps Mnumber of times, it is possible to produce a distribution of expectedvalues for the chosen levels of the independent variables that incorporates both types of uncertainty into theinterpretation. Comparing the expected value distributions for different values of the independent variablesallows us to see the substantive impact of these variables. In particular, graphical display of these expectedvalue distributions can clearly depict these relationships (Cleveland 1993).

Implementation in the paper

In order to use the method described above, it is necessary to choose levels of the independent variables atwhich you want to see effects. In the negative binomial model used in this paper, the effect of number of

discussants on the expected value of the participation variable depends on the level of all the othervariables. In order to produce realistic comparisons for low and high status people, I chose levels of theindependent variables that are typical for individuals with a high school education and a college education.These were obtained by regressing each of the independent variables (other than number of discussants) oneducation and predicting their value when years of education equalled 12 and 16, respectively. Table Breports these values.


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Table B. Expected Value of Independent Variables for High School and College

Educated Respondents. This table presents the expected value for each of theindependent variables at particular levels of education. These are the values at whichthese variables were set in producing the expected value distributions used in Figure 3and Figure 4.

Variable Value when Education =

12 Years 16 Years

Interest in 1.32 1.44

Politics

Income 4.16 5.87

Age 50.43 45.87

Party contact 0.24 0.27

Church Attendance 2.49 2.61

Group Membership 0.94 0.94

Married 0.72 0.84

Partisan Extremity 1.85 1.97

Source: 1984 South Bend Election Study (Huckfeldt and Sprague 1985).


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Socia Networks and Political Participation_ the Role of Social in(2)

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